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Trend Spotting and Pattern Spott...

<h3>About Training</h3><p>Data visualization is an important part of analytics. Analytics effectiveness and impact depends on visualization skills of two kinds – ability to create visuals and ability to understand visuals. The real value of visualization does not come from creating visuals, but from understanding what they can tell you. With the language of words we learn reading and writing as separate but related skills. Similarly, with visual language we need to learn understanding (reading) and creating (writing) as distinct but related skills. There are many books, courses, and other resources that teach people how to develop data visualizations but few that teach how to read and understand them. This course aims to fill that gap by teaching the core capabilities of understanding and interpreting data visualizations.</p><br /><h3>What You'll Learn</h3><ul><li><span>Ten key concepts of data visualization</span></li><li><span>The most important things to look for when reading visualizations</span></li><li><span>How to do a “quick read” of data visualizations</span></li><li><span>How to do a “critical read” of data visualizations</span></li><li><span>To see trends, patterns, and outliers in visual presentation of data</span></li><li><span>To see ambiguity, distortion, and bias in visual presentation of data</span></li></ul><br /><h3>Who Should Attend</h3><ul><li><span>Business managers, decision makers, analysts and other analytics consumers seeking to refine their skills for understanding data visualizations</span></li><li><span>Data scientists, data analysts, and other analytics providers seeking to enhance their data visualization skills by understanding visualization from the perspective of the readers</span></li><li><span>Developers of data visualizations who will improve visualization skills by seeing data visualization through the eyes of the readers</span></li></ul><br /><h3>Outline</h3><p><strong>Part 1: Visual Language</strong></p><ul><li>About Language<ul><li>Word Languages</li><li>Numeric Languages</li><li>Visual Languages</li></ul></li><li>Visual Language<ul><li>Parts, Structure, and Rules</li><li>Visual Communication</li><li>Data Visualization Language</li><li>Interactive Visualization</li></ul></li></ul><p><strong>Part 2: Data Visualization Concepts</strong></p><ul><li>Chart and Graph Formats<ul><li>Static</li><li>Interactive</li><li>Animated</li><li>Narrated (Storytelling)</li></ul></li><li>Chart and Graph Types<ul><li>Basic Types: Line, Column, Bar, Area, Scatter, Bubble, Map, etc.</li><li>Beyond Basics: Tree Maps, Heat Maps, Sunbursts, Clouds, etc.</li></ul></li><li>Patterns and Trends<ul><li>Pattern Basics: Center, Spread, Peaks, Mode, Skew, etc.</li><li>Patterns, Trends, and Chart Types</li></ul></li><li>Data<ul><li>Data Sets</li><li>Things, Instances, and Variables</li><li>Data Sources and Lineage</li></ul></li><li>Visual Composition<ul><li>Axes and Scales</li><li>Legends</li></ul></li></ul><p><strong>Part 3: Quick Reading of Data Visualizations</strong></p><ul><li>Finding Context<ul><li>First Impression</li><li>Title</li><li>Axes</li><li>Scales</li><li>Legend</li></ul></li><li>Looking at the Data<ul><li>Variables and Quantities</li><li>Meaning</li><li>Quick Read Process</li><li>Process Summary</li><li>Quick Read Exercises</li></ul></li></ul><p><strong>Part 4: Critical Reading of Data Visualizations</strong></p><ul><li>First Glance<ul><li>From a Distance</li><li>Organization</li><li>Visual Appeal</li></ul></li><li>Source of the Chart<ul><li>Data Sources</li><li>Data Analyst</li><li>Data Analysis</li></ul></li><li>You and the Chart<ul><li>Format and Interaction</li><li>Connections</li></ul></li><li>Reading the Chart<ul><li>Title</li><li>Type</li><li>Layout: Aspect Ratio, Legend, Colors, Patterns, Annotation, etc.</li><li>Data: Variables, Encodings, Relationships, etc.</li><li>Axes and Scales: Variables, Coordinates, Units, Intervals, etc.</li><li>Considering Outliers</li></ul></li><li>Finding the Meaning<ul><li>Considering Outliers</li><li>Finding Patterns and Trends</li><li>Critical Read Exercises</li></ul></li></ul><br />

<h3>About Training</h3><p>Data visualization is an important part of analytics. Analytics effectiveness and impact depends on visualization skills of two kinds – ability to create visuals and ability to understand visuals. The real value of visualization does not come from creating visuals, but from understanding what they can tell you. With the language of words we learn reading and writing as separate but related skills. Similarly, with visual language we need to learn understanding (reading) and creating (writing) as distinct but related skills. There are many books, courses, and other resources that teach people how to develop data visualizations but few that teach how to read and understand them. This course aims to fill that gap by teaching the core capabilities of understanding and interpreting data visualizations.</p><br /><h3>What You'll Learn</h3><ul><li><span>Ten key concepts of data visualization</span></li><li><span>The most important things to look for when reading visualizations</span></li><li><span>How to do a “quick read” of data visualizations</span></li><li><span>How to do a “critical read” of data visualizations</span></li><li><span>To see trends, patterns, and outliers in visual presentation of data</span></li><li><span>To see ambiguity, distortion, and bias in visual presentation of data</span></li></ul><br /><h3>Who Should Attend</h3><ul><li><span>Business managers, decision makers, analysts and other analytics consumers seeking to refine their skills for understanding data visualizations</span></li><li><span>Data scientists, data analysts, and other analytics providers seeking to enhance their data visualization skills by understanding visualization from the perspective of the readers</span></li><li><span>Developers of data visualizations who will improve visualization skills by seeing data visualization through the eyes of the readers</span></li></ul><br /><h3>Outline</h3><p><strong>Part 1: Visual Language</strong></p><ul><li>About Language<ul><li>Word Languages</li><li>Numeric Languages</li><li>Visual Languages</li></ul></li><li>Visual Language<ul><li>Parts, Structure, and Rules</li><li>Visual Communication</li><li>Data Visualization Language</li><li>Interactive Visualization</li></ul></li></ul><p><strong>Part 2: Data Visualization Concepts</strong></p><ul><li>Chart and Graph Formats<ul><li>Static</li><li>Interactive</li><li>Animated</li><li>Narrated (Storytelling)</li></ul></li><li>Chart and Graph Types<ul><li>Basic Types: Line, Column, Bar, Area, Scatter, Bubble, Map, etc.</li><li>Beyond Basics: Tree Maps, Heat Maps, Sunbursts, Clouds, etc.</li></ul></li><li>Patterns and Trends<ul><li>Pattern Basics: Center, Spread, Peaks, Mode, Skew, etc.</li><li>Patterns, Trends, and Chart Types</li></ul></li><li>Data<ul><li>Data Sets</li><li>Things, Instances, and Variables</li><li>Data Sources and Lineage</li></ul></li><li>Visual Composition<ul><li>Axes and Scales</li><li>Legends</li></ul></li></ul><p><strong>Part 3: Quick Reading of Data Visualizations</strong></p><ul><li>Finding Context<ul><li>First Impression</li><li>Title</li><li>Axes</li><li>Scales</li><li>Legend</li></ul></li><li>Looking at the Data<ul><li>Variables and Quantities</li><li>Meaning</li><li>Quick Read Process</li><li>Process Summary</li><li>Quick Read Exercises</li></ul></li></ul><p><strong>Part 4: Critical Reading of Data Visualizations</strong></p><ul><li>First Glance<ul><li>From a Distance</li><li>Organization</li><li>Visual Appeal</li></ul></li><li>Source of the Chart<ul><li>Data Sources</li><li>Data Analyst</li><li>Data Analysis</li></ul></li><li>You and the Chart<ul><li>Format and Interaction</li><li>Connections</li></ul></li><li>Reading the Chart<ul><li>Title</li><li>Type</li><li>Layout: Aspect Ratio, Legend, Colors, Patterns, Annotation, etc.</li><li>Data: Variables, Encodings, Relationships, etc.</li><li>Axes and Scales: Variables, Coordinates, Units, Intervals, etc.</li><li>Considering Outliers</li></ul></li><li>Finding the Meaning<ul><li>Considering Outliers</li><li>Finding Patterns and Trends</li><li>Critical Read Exercises</li></ul></li></ul><br />

Training Details

Training Time

Capacity

Prerequisites

Documents

About Training

Data visualization is an important part of analytics. Analytics effectiveness and impact depends on visualization skills of two kinds – ability to create visuals and ability to understand visuals. The real value of visualization does not come from creating visuals, but from understanding what they can tell you. With the language of words we learn reading and writing as separate but related skills. Similarly, with visual language we need to learn understanding (reading) and creating (writing) as distinct but related skills. There are many books, courses, and other resources that teach people how to develop data visualizations but few that teach how to read and understand them. This course aims to fill that gap by teaching the core capabilities of understanding and interpreting data visualizations.

What You'll Learn

Ten key concepts of data visualization

The most important things to look for when reading visualizations

How to do a “quick read” of data visualizations

How to do a “critical read” of data visualizations

To see trends, patterns, and outliers in visual presentation of data

To see ambiguity, distortion, and bias in visual presentation of data

Who Should Attend

Business managers, decision makers, analysts and other analytics consumers seeking to refine their skills for understanding data visualizations

Data scientists, data analysts, and other analytics providers seeking to enhance their data visualization skills by understanding visualization from the perspective of the readers

Developers of data visualizations who will improve visualization skills by seeing data visualization through the eyes of the readers

Outline

Part 1: Visual Language

About Language

Word Languages

Numeric Languages

Visual Languages

Visual Language

Parts, Structure, and Rules

Visual Communication

Data Visualization Language

Interactive Visualization

Part 2: Data Visualization Concepts

Chart and Graph Formats

Static

Interactive

Animated

Narrated (Storytelling)

Chart and Graph Types

Basic Types: Line, Column, Bar, Area, Scatter, Bubble, Map, etc.

Beyond Basics: Tree Maps, Heat Maps, Sunbursts, Clouds, etc.

Patterns and Trends

Pattern Basics: Center, Spread, Peaks, Mode, Skew, etc.

Patterns, Trends, and Chart Types

Data

Data Sets

Things, Instances, and Variables

Data Sources and Lineage

Visual Composition

Axes and Scales

Legends

Part 3: Quick Reading of Data Visualizations

Finding Context

First Impression

Title

Axes

Scales

Legend

Looking at the Data

Variables and Quantities

Meaning

Quick Read Process

Process Summary

Quick Read Exercises

Part 4: Critical Reading of Data Visualizations

First Glance

From a Distance

Organization

Visual Appeal

Source of the Chart

Data Sources

Data Analyst

Data Analysis

You and the Chart

Format and Interaction

Connections

Reading the Chart

Title

Type

Layout: Aspect Ratio, Legend, Colors, Patterns, Annotation, etc.

Data: Variables, Encodings, Relationships, etc.

Axes and Scales: Variables, Coordinates, Units, Intervals, etc.

Considering Outliers

Finding the Meaning

Considering Outliers

Finding Patterns and Trends

Critical Read Exercises

You can also request this training at your institution as a private class. Please contact us:

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